Method and system for automatic speech recognition

ABSTRACT

A method of recognizing speech is provided that includes generating a decoding network that includes a primary sub-network and a classification sub-network. The primary sub-network includes a classification node corresponding to the classification sub-network. The classification sub-network corresponds to a group of uncommon words. A speech input is received and decoded by instantiating a token in the primary sub-network and passing the token through the primary network. When the token reaches the classification node, the method includes transferring the token to the classification sub-network and passing the token through the classification sub-network. When the token reaches an accept node of the classification sub-network, the method includes returning a result of the token passing through the classification sub-network to the primary sub-network. The result includes one or more words in the group of uncommon words. A string corresponding to the speech input is output that includes the one or more words.

RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2013/087816, entitled “METHOD AND SYSTEM FOR AUTOMATIC SPEECHRECOGNITION” filed Nov. 26, 2013, which claims priority to ChinesePatent Application No. 201310037464.5, entitled “METHOD AND SYSTEM FORAUTOMATIC SPEECH RECOGNITION,” filed Jan. 30, 2013, both of which areherein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present application relates generally to the technical field ofautomatic speech recognition (ASR), and specifically relates to a methodand system for automatic speech recognition.

BACKGROUND OF THE INVENTION

Automatic speech recognition is an area of technology which transformsthe lexical content of human speech into an input form (e.g., acharacter string) that can be read by computers. The process ofautomatic speech recognition typically includes several operations,including: generating a language model that contains a plurality ofwords in a corpus, training an acoustic model to create statisticalrepresentations of one or more contrastive units of sound (called“phonemes” or simply “phones”) that make up each word in the corpus,building a decoding network (sometimes called a “decoding resourcenetwork) using the language model and the acoustic model, and finallydecoding human speech.

FIG. 1 is a schematic diagram of a processing flow using a conventionalautomatic speech recognition system. In some circumstances, theprocessing flow is performed at a speech recognition device. Referringto FIG. 1, the processing flow includes:

Operation 101 and 102, in which an acoustic model is trained using soundsamples. Similarly a language model is trained using a corpus.

The acoustic model is one of the most important aspects of a speechrecognition system. Most of the mainstream speech recognition systemsadopt Hidden Markov Models (HMM) to construct acoustic models. An HMM isa statistical model which is used to describe a Markov processcontaining a hidden parameter (e.g., a parameter that is not directlyobserved). In an HMM, although the hidden parameter is not directlyobserved, one or more variables affected by the hidden parameter areobserved. In the context of speech recognition, a spoken phoneme isconsidered a hidden parameter, whereas acoustic data received (e.g., bya microphone of the device) is the observed variable. The correspondingprobability between the spoken phoneme and the acoustic data isdescribed in the acoustic model (e.g., the acoustic model describes theprobability that acoustic data was generated by a user speaking aparticular phoneme).

In some circumstances, a speech signal received by the device isexpressed (e.g., represented) as a triphone. For example, such atriphone can be constructed by including a current phone as well asright and left half phones adjacent to the current phone.

The main structure of the language model is a probability distributionp(s) of a character string s, reflecting the probability of thecharacter string s appearing as a sentence. Suppose w_(i) stands for thei^(th) word in the character string s. In this case, the probabilitydistribution p(s) can be written as:

p(s)=p(w ₁ w ₂ w ₃ . . . w _(n))=p(w ₁)p(w ₂ |w ₁)p(w ₃ w ₁ |w ₂) . . .p(wk|w ₁ w ₂ . . . w _(k-1))

Operation 103, in which a decoding resource network is constructedaccording to the acoustic model, language model and a presupposeddictionary. In some circumstances, the decoding resource network is aweighted finite state transducer (WFST) network.

Operation 104, in which speech is input into the decoder, the speech isdecoded by the decoder according to the decoding resource network, and acharacter string with the highest probability value is output as therecognized result of the speech input.

FIG. 2 is a flowchart diagram of a method for constructing a decodingresource network using conventional technology. Referring to FIG. 2, themethod includes: obtaining an Ngram WFST network by transforming thelanguage model, obtaining a Lexicon-WFST by transforming the dictionary,and obtaining a Context-WFST network by transforming the acoustic model.These three WFST networks are merged, for example, by first merging theNgram-WFST network and the Lexicon-WFST network into a Lexicon-Gram-WFSTnetwork, then merging the Lexicon-Gram-WFST with the Context-WFSTnetwork. Finally the decoding resource network is obtained. In thisexample, the decoding resource network is a Context-Lexicon-Gram-WFSTnetwork.

However, most conventional speech recognition technology is based on auniversal speech recognition application that constructs model based oncommon speech. In this situation, the corpus used to the train thelanguage model is based on data collected through the actual input ofusers. Though the speech habits of users are well reflected in such amodel, these models struggle to recognize less frequently used (e.g.,obscure) words, such as personal names, medicinal names, place names,etc. This is because the probability value of the character stringcorresponding to the obscure words in the language model is very low.When conventional speech recognition systems need to recognize obscurewords spoken by the user, they too often fail.

Thus, what is needed is speech recognition technology (e.g., methods andsystems) which are more easily able to recognize the use of obscurewords.

SUMMARY

To address the aforementioned problems, some implementations of thepresent application provide a computer-implemented method of method ofrecognizing speech. The method includes generating a decoding networkfor decoding speech input. The decoding network includes a primarysub-network and one or more classification sub-networks. The primarysub-network includes a plurality of classification nodes. Eachclassification node corresponds to a respective classificationsub-network of the one or more classification sub-networks. Furthermore,each classification sub-network of the one or more classificationsub-networks corresponds to a group of uncommon words. The methodfurther includes receiving a speech input and decoding the speech inputby instantiating a token corresponding to the speech input in theprimary sub-network and passing the token through the primary network.When the token reaches a respective classification node of the pluralityof classification nodes, the method includes transferring the token tothe corresponding classification sub-network and passing the tokenthrough the corresponding classification sub-network. When the tokenreaches an accept node of the classification sub-network, the methodincludes returning a result of the token passing through theclassification sub-network to the primary sub-network. The resultincludes one or more words in the group of uncommon words correspondingto the classification sub-network. Finally, the method includesoutputting a string corresponding to the speech input that includes theone or more words.

In another aspect of the present application, to address theaforementioned problems, some implementations provide a non-transitorycomputer readable storage medium storing one or more programs. The oneor more programs comprise instructions, which when executed by anelectronic device with one or more processors and memory, cause theelectronic device to perform any of the methods provided herein.

In yet another aspect of the present application, to address theaforementioned problems, some implementations provide an electronicdevice. The electronic device includes one or more processors, memory,and one or more programs. The one or more programs are stored in memoryand configured to be executed by the one or more processors. The one ormore programs include an operating system and instructions that whenexecuted by the one or more processors cause the electronic device toperform any of the methods provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding, reference should be made to the followingdetailed description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a schematic diagram of a processing flow used in someconventional automatic speech recognition systems.

FIG. 2 is a flowchart diagram for construction of a decoding resourcenetwork used in some conventional automatic speech recognition systems.

FIG. 3 is a flowchart diagram of an automatic speech recognition method,in accordance with some implementations.

FIG. 4 is a flowchart diagram of a method for constructing a primarydecoding sub-network, in accordance with some implementations.

FIG. 5 is an example of a content schematic diagram of a generallanguage model, in accordance with some implementations.

FIGS. 6A and 6B are partial schematic diagrams of a transformation fromthe general language model shown in FIG. 5 into an Ngram-WFST network.

FIG. 7 is a partial schematic diagram of content contained in adictionary, in accordance with some implementations.

FIGS. 8A and 8B are partial schematic diagrams of a transformation fromthe dictionary content in FIG. 7 into a Lexicon-WFST network.

FIG. 9 is a partial schematic diagram of a transformation from anacoustic model into a Content-WFST network, in accordance with someimplementations.

FIG. 10 is a flowchart diagram of a method for constructing aclassification sub-network, in accordance with some implementations.

FIG. 11 is a partial schematic diagram of a transformation from anacoustic model into a Content-WFST network, in accordance with someimplementations.

FIG. 12 is a flowchart diagram of a decoding process, in accordance withsome implementations.

FIG. 13 is a schematic diagram of a speech recognition system, inaccordance with some implementations.

FIG. 14 is a schematic diagram of a primary network constructing module,in accordance with some implementations.

FIG. 15 is a schematic diagram of a sub-network constructing module, inaccordance with some implementations.

FIG. 16 is a schematic diagram of a decoder, in accordance with someimplementations.

FIGS. 17A and 17B are schematic flowcharts of a method for recognizingspeech commands, in accordance with some implementations.

FIG. 18 is a diagram of a client-server environment for speech commandrecognition, in accordance with some implementations.

FIG. 19 is a block diagram illustrating a speech command recognitionserver system, in accordance with some implementations.

FIG. 20 is a block diagram illustrating a client device, in accordancewith some implementations.

Like reference numerals and names refer to corresponding partsthroughout the drawings.

DESCRIPTION OF EMBODIMENTS

Reference will now be made in detail to various implementations,examples of which are illustrated in the accompanying drawings. In thefollowing detailed description, numerous specific details are set forthin order to provide a thorough understanding of the present disclosureand the described implementations herein. However, implementationsdescribed herein may be practiced without these specific details. Inother instances, well-known methods, procedures, components, andmechanical apparatus have not been described in detail so as not tounnecessarily obscure aspects of the implementations.

FIG. 3 is a flowchart diagram of an automatic speech recognition method,in accordance with some implementations. The method includes:

Operation 301, classification processing of words. In operation 301,words are classified (e.g., as personal names, medicinal names, placenames, or other classifications of words). Each classification isassigned a classification mark, and obscure words are replaced by theirclassification symbol.

Operation 302, construct a primary decoding sub-network. In operation301: a primary decoding sub-network is constructed according to a speechmodel, a primary dictionary and a general language model. Duringconstruction of the primary decoding sub-network, classifying markednodes (corresponding to the classification marks of operation 301) areadded to the primary decoding sub-network, and the classifying markednodes are connected with other nodes by network edges (sometimesreferred to simply as “edges”).

Operation 303, construct one or more classification sub-networks. Inoperation 303, a classification sub-networks is constructedcorresponding to each classification. For each classification, there isa classification language model, a classification dictionary, and aclassification acoustic model and the classification sub-network isconstructed therefrom (e.g., in accordance with to the classificationlanguage model, the classification dictionary, and the classificationacoustic model). Each classification sub-network is linked to theprimary decoding sub-network via one or more phones as a startingposition (e.g., a coda phone and a mute phone in the primary dictionary)and one or more phones as an ending position (e.g., an onset phone and amute phone of each word in the primary dictionary).

Operation 304, decoding speech with a decoder. In some implementations,operation 304 includes decoding and recognizing a speech input accordingto the primary decoding sub-network. When a decoding token meets withthe classifying marked nodes, operation 304 includes preserving the edgeand a phone before the classifying marked node and jumping to thecorresponding classification sub-network of the classifying marked node.Operation 304 further includes taking the phone before the classifyingmarked nodes as an index to find the starting position of theclassification sub-network, then decoding and recognizing the speechinput in the classification sub-network from the starting position upuntil and ending position is reached. Operation 304 further includespreserving the phone before the ending position, then jumping back tothe edge before the classifying marked node in the primary decodingsub-network, and taking the phone before the ending position as an indexto find the starting position of the subsequent decoding process.Decoding and recognition of the speech input is continued from thatstarting position. The decoding result is output. In someimplementations, the character string with the highest probable value isoutput and hence is the recognized results of decoding the speech input.In some implementations, a final speech recognition results is outputafter further processing of the output result of decoding.

In some implementations, during operation 301, words in a raw corpus areclassified so as to obtain different classifications of words. Words thecorpus can be divided into classifications, such as person names, placenames, computer terminology, medical terminology, etc. For example,“isatis root” may belong to the classification of medical terminology. Aword may also belong to multiple classifications. However, because theamount of time spent classifying words shows an exponential growth withthe number of classifications to which a word may belong, in someimplementations words are classified according to, at most, oneclassification. In some implementations, a respective word is classifiedin accordance with a probability that the respective word belongs to arespective classification, e.g., in accordance with max(p(w|Ci)), inwhich w indicates the respective word, and Ci indicates a respectiveclassification. In some implementations, commonly used words are notclassified (e.g., classification of commonly used words is forgone).

As shown in Table 1, the left half of Table 1 is the general languagemodel before replacement, among which “isatis root” and “pseudo ginseng”belong to a classification of obscure medical terminology having acorresponding classifying mark is C1. “Andy Liu” belongs to aclassification of obscure person names having a correspondingclassifying mark is C2. The right half of Table 1 is the generallanguage model after replacement, in which the obscure words in theobscure medical terminology and obscure names classifications have beenreplaced with their respective classifying mark. In someimplementations, a rollback probability of each obscure word in suchclassifications is calculated. When decoding, in case that the tokenencounters multiple classifying mark nodes, the decoder enters theclassification sub-network corresponding to a classifying mark that hasa rollback probability exceeding a predetermined threshold value.

TABLE 1 ngram 1 = 5 ngram 1 = 5 ngram 2 = 8 ngram 2 = 8 ngram 3 = 7ngram 3 = 7 \1-grams \1-grams −0.415 </s> −0.415 </s> −99 <s> −1.9 −99<s> −1.9 −0.6356 isatis root −3.42 −0.6356 C1 −3.42 −0.434 pseudoginseng −3.24 −0.434 C1 −3.24 −0.11 you −1.23 −0.11 you −1.23 −0.2 AndyLiu −2.1 −0.2 C2 −2.1 \2-grams \2-grams −0.42 good day −3.42 −0.42 goodday −3.42 −0.3 isatis root good −2.3 −0.3 C1 good −2.3

In some implementations, during construction of classificationsub-networks in the Operation 303, classification sub-networks arelinked to the primary decoding sub-network by in one of several ways.For example, in some implementations, classification sub-networks arelinked to the primary decoding sub-network by taking the last twomonophones of each phrase in the primary decoding sub-network as thestarting position. Or, for example, when linking a classificationsub-network to a monophone word (e.g., “an”) in the primary decodingsub-network, a mute phone plus the phone of the monophone word (or,alternatively, just the mute phone) is taken as the starting position.In some implementations, two onset monophones of each phrase in theprimary decoding sub-network are taken as an ending position. In somecircumstances, the phone of a monophone word plus a mute are taken asthe ending position (or, alternatively, just the mute phone).

In some implementations, operation 304 includes decoding and recognizingthe speech input according to the primary decoding sub-network. When adecoding token meets with a classifying marked node, the edge of theprimary decoding sub-network is preserved along with one or two phonebefore the classifying marked node. The token jumps to the correspondingclassification sub-network of the classifying marked node. Operation 304then includes taking the one or two phones before the classifying markednodes as an index to find a starting position of the classificationsub-network and decoding (e.g., recognizing) the speech input in theclassification sub-network from that staring position up until theending position. Operation 304 then includes preserving one or twophones before the ending position and jumping back to the edge beforethe classifying marked node in the primary decoding sub-network.Operation 304 then includes taking one or two phones before the endingposition as an index to find the starting position for subsequentdecoding, and continuing to decode and recognize the speech input fromthat starting position. Operation 304 further includes outputting thedecoding results, which, in some implementations, entails, outputting acharacter string with the highest probable value as the recognizedresults of the speech input. In some implementations, a final recognizedresult is output after further processing.

Attention is now directed to construction of the primary decodingsub-network and the classification sub-network, respectively.

FIG. 4 is a flowchart diagram of a method for constructing the primarydecoding sub-network, in accordance with some implementations. Referringto FIG. 4, the flowchart diagram includes:

Operation 401, transform the general language model into a correspondingWFST network.

FIG. 5 is an example of a content schematic diagram of a generallanguage model, in accordance with some implementations. FIGS. 6A and 6Bare partial schematic diagrams of a transformation from the generallanguage model shown in FIG. 5 into an Ngram-WFST network. Because theNgram-WFST network is complex, only part of it is shown. Referring toFIG. 5 and FIGS. 6A and 6B, the general language model and theNgram-WFST include the character strings of “ni,” “hao,” “ni hao NAME,”etc. and a probability value corresponding to each character string. Asshown in FIG. 5, “NAME” is the classifying mark of a person nameclassification. (Note on translation: “Ni hao” is a Chinese greetingakin to “Hello” in English, “Chi le ma” translates roughly to “Have youeaten?” or, in more colloquial usage, “How are you?”) The symbol <s>refers to a starting mute phone, </s> refers to an ending mute phone.The symbol #phi indicates a non-labeled edge, meaning that a token ispassed from one node to another node via a non-labeled edge without theaddition of any recognized speech output. The transformation from thegeneral language model into the corresponding WFST network in thepresent application can be realizing using conventional transformationmethods which are known in the art. Using these, relevant syntacticstructure can also be transformed into the corresponding WFST.

Referring again to FIG. 4, the flowchart diagram includes operation 402:transform the primary dictionary into a corresponding WFST network. Insome implementations, operating 402 includes adding an edgecorresponding to the classifying marks between a starting symbol and anending symbol, and marking the input and output symbols of thecorresponding edges according to the classification.

FIG. 7 is a partial schematic diagram of content contained in adictionary, in accordance with some implementations. FIG. 7 showssymbols on the left (e.g., <s>, </s> “ni_hao,” and “chi_le_ma”)corresponding to dictionary entries. To the right of each symbol is acorresponding phonetic notation (e.g., “si1” the phonetic notation forthe mute symbol while “ch,” “ix1” “1” e4” “m” “a2” each representphonetic sounds in the Chinese language). FIGS. 8A and 8B are partialschematic diagrams of a transformation from the dictionary content inFIG. 7 into a Lexicon-WFST network.

Referring to FIGS. 8A and 8B, each edge in the Lexicon-WFST network hasan input and an output symbol (e.g., an input label and an outputlabel). In the WFST network corresponding to the primary dictionary, theclassifying mark “NAME” is the same with every word, with its individualedge. However, both input and output symbols of its corresponding edgeare such classifying mark, in which, the input label is not the sequenceof monophone, but it is just the same classifying mark “NAME” with itsoutput label.

Operation 403, transform the speech model into corresponding WFSTnetwork. During the process of transformation of WFST network, thedifference of the present application lies in that for each phone doublenodes (each phone double nodes consists of two sequential monophones),edge pointing to each classifying marked node is required to beindicated, among which, input and output symbols of edge pointing toeach classifying marked node are classifying marks; two sequential edgesafter the each classifying marked node: taking monophone as edge ofinput and output symbols.

For example, FIG. 9 serves as a partial schematic diagram of a WFSTnetwork from the transformation of a speech model. In the presentapplication, WFST network, corresponding to the speech model,transformed by the adoption of the conventional approach can beutilized, but the difference lies in the requirement for the additionalintroduction of classifying marked edge during transformation, that is:the node corresponding to each phone, as (si1, n), (n, i3), (i3, h), (h,aa3) and (aa3, uu3) shown in the figure, will lead a classifying markededge to the corresponding classifying marked node (NAME), and all ofinput and output symbols of this edge are classifying label NAME; thenleading an edge from classifying marked node (NAME) to any possible nodesuch as (NAME, ch), phone ch acting not only as input label of thisedge, but also output label of this edge, that is to say, this edge usesmonophone ch as its input and output symbols; for the next edge afterthe node (NAME, ch), for example, leading an edge from the node (NAME,ch) to (ch, ix1), both input label and output label are monophone ix1,namely, this edge serves as an edge using monophone ix1 as input andoutput symbols. When the process is completed, lead classifying mark inclassifying language model of type of NAME to WFST network.

Operation 404, merge the speech model, primary dictionary with WFSTnetwork from the transformation of language model, and the mergingprocess includes WFST Compose, determinize and other operations forobtaining a WFST network as the primary decoding sub-network.

Naturally, it is not necessary to limit the order of operations of 401,402, 403, and other orders are acceptable, or simultaneous performanceis allowed. Naturally, at first, the language model can merge with WFSTnetwork corresponding to primary dictionary, after that, it can mergewith WFST network corresponding to the speech model.

FIG. 10 is a flowchart diagram showing a specific method of constructingclassification sub-network. Based on the speech model, primarydictionary, sub network dictionary and the classifying language model ofeach classification, construct each classification sub-networkcorresponding to each classifying language model, among which, performthe flow in FIG. 10 for each classification, including:

Operation 111, transform classifying language model of theclassification into corresponding sub WFST network. This process is thesame with the process of transforming the current language model intoWFST network, among which, corresponding syntactic structure also can betransformed into the corresponding WFST.

Operation 112, transform the sub network dictionary into correspondingsub WFST network, and in this process, the corresponding path of wordsin sub network dictionary is the same as the conventional WFSTtransforming ways of the dictionary. However in the present application,among the phonetic symbol corresponding to start symbol, as shown inFIG. 7, except original mute (identified by symbol <s>, correspondingphonetic notation is si1), it also includes further: in the primarydictionary, phone pairs consisting of the last two phones of each word,phone pairs consisting of phones of each monophonemic word and the lastone phone of each of other words, combination of mute and phones of eachmonophonemic word. Phonetic symbol corresponding to end symbol, exceptoriginal mute (identified by symbol </s>, corresponding phoneticnotation is si1), also includes further: in the primary dictionary,phone pairs consisting of the first two phones of each word, phone pairsconsisting of phones of each monophonemic word and one phone at thebeginning of each of other words, and combination of mute and phones ofeach monophonemic word.

In this way, it enables to correspond to its corresponding content ofprimary dictionary in starting and ending positions.

Operation 113, transform the speech model into corresponding sub WFSTnetwork. For example, FIG. 11 serves as a kind of partial schematicdiagram of sub WFST network from the transformation of a kind of speechmodel. In the present application, sub WFST network, corresponding tothe speech model, transformed by the adoption of the conventionalapproach, can be utilized, but the difference is that during the processof transformation, for link therein: taking two sequential edgesconsisting of the last two monophones of each word in primary dictionaryand with monophone as input and output symbols as the starting position,or taking edge with mute as input and output symbols linking up edgewith phone of each monophonemic word in primary dictionary as input andoutput symbols as the starting position, or taking edge with mute asinput and output symbols as the starting position; taking two sequentialedges consisting of the first two monophones of each word in primarydictionary and with monophone as input and output symbols as the endingposition, or taking edge with phone of each monophonemic word in primarydictionary as input and output symbols linking up edge with mute asinput and output symbols as the ending position, or taking edge withmute as input and output symbols as the ending position.

When constructing primary decoding sub-network, there is only one edgeleading to (eps, si1) from starting node, while for the constructing ofclassification sub-network, there is one (mute edge) or two edges (oneis mute edge, another is edge with monophone as input and outputsymbols) from starting node to pronunciation node so that it can belinked up in the corresponding position.

For example, FIG. 11 is a schematic diagram of transformation from aspeech model into portion of a WFST network. In particular, FIG. 11which shows a portion of the WFST network that outputs the phrase “Nihao, Lisi, chi le ma?” (Note: “Ni hao” is a Chinese greeting akin to“Hello” in English, “Chi le ma” translate to “Have you had your dinner?”or, in more colloquial usage, “How are you?” while “Lisi” is a name) Inwhich, “Lisi,” as a kind of link, the starting position of the linktaking edge of two sequential monophones input and output symbolsconsisting of pronunciations (aa3 and uu3) of last two monophones of theword “hao” as the starting position, that is, at first, it is the edgewith monophone as input and output symbols (both input symbol and outputsymbol of the edge are aa3), then it is followed by an edge withmonophone as input and output symbols (both input symbol and outputsymbol of the edge are uu3). In the ending position, taking the twosequential edges consisting of the first two monophones (ch and ix1) ofthe word “chi” and with monophone as input and output symbols as theending position, namely, phone pairs (ch, ix1) of the node “chi” isfollowed by an edge with monophone ch as input and output symbols (bothinput symbol and output symbol of the edge are ch), then followed by anedge with monophone ix1 as input and output symbols (both input symboland output symbol of the edge are ix1), the two sequential edges withmonophone as input and output symbols indicate the arrival at end pointof the link, demanding for jumping out of classification sub-network.

Operation 114, merge the speech model, sub network dictionary with WFSTnetwork from the transformation of language model of the classificationfor obtaining a WFST network as the classification sub-networkcorresponding to the classification.

Naturally, it is not necessary to limit the order of operations of 111,112, 113, and other orders are acceptable, or simultaneous performanceis allowed. Naturally, at first, the language model can merge with subWFST network corresponding to sub network dictionary, after that, it canmerge with sub WFST network corresponding to the speech model.

FIG. 12 is a specific embodiment flowchart diagram of the decodingprocess in the present application, refer to FIG. 12, and the flowincludes in detail:

Operation 121, decoding and recognizing the speech input is performedaccording to the primary decoding sub-network, when the decoding tokenmeets with the edge which regards the classifying marks as input andoutput symbols, determine the met classifying marked node, and take theedge which regards the classifying mark as input and output symbol asthe edge before the classifying marked node for saving, and save one ortwo monophones before the classifying marked node. The situation of onephone refers to si1 as phone of mute.

Operation 122, jumping to the classifying classification sub-networkcorresponding to the classifying mark, taking one or two phones beforeclassifying marked node as indexes to find the one or two continuousedges which regard the one or two phones as input and output symbols inthe classification sub-network, take the one or two continuous edges asstarting position, and decoding and recognize the speech input inclassification sub-network from that starting position; when thedecoding token meets with the edge which regards monophone as input andoutput symbols, saving the monophone of the edge until reaching theending position.

Operation 123, jumping back to the edge before classifying node of theprimary decoding sub-network, and take the one or two monophones savedbefore ending position as indexes to find the one or two continuousedges after classifying marked node which regard the one or twomonophones before ending position as input and output symbolsrespectively, take the reached nodes of the one or two continuous edgesas starting position of subsequent decoding, continue to decode andrecognize the speech input from that starting position.

Operation 124, output decoding results, which specifically includes:output the character string with highest probable value as therecognizing results of the speech input; or output the final speechrecognizing results after further processing of the output results indecoding.

For example, the following content is about the specific process ofdecoding by decoder when user inputs speech “ni hao, Lisi, chi le ma?”.

Herein, as WFST network merging with language model and dictionary isover complicated, this example only takes WFST corresponding to speechmodel as model for demonstration. For example, decoder can use WFSTnetwork shown in the FIG. 9 and sub WFST network in FIG. 11 to decoding,and the specific process of decoding is as follows:

At first, it begins with 0 node of primary decoding sub-network shown inFIG. 9, decoding token through edge with blank (<eps>) input label istransferred to si1−n+i3, after the matching with this triphones is done,it is transferred to n−i3+h, and finally reaching node (aa3, uu3), andherein, it meets special edge with its input label as classifying markNAME, and it therefore saves on-site information, the on-siteinformation including output on this special edge and its former twoedges, namely, two monophones aa3 and uu3. Then jump to classificationsub-network in FIG. 11, using the saved front monophone (namely aa3,uu3) in on-site information to find corresponding starting and endingpositions (that is edge in sub network with monophone aa3 as input andoutput symbols, and edge with monophone uu3 as input and output symbols,after that start to spread from node (aa3, uu3). When decoding tokenmeets node (ch, ix1), it will also come across edge with monophone asinput label (namely, edge with monophone ch as input and outputsymbols), and at the moment, save this monophone ch followed byspreading downwards, if meeting edge with monophone as input label(namely, edge with monophone ix1 as input and output symbols), monophoneix1 shall be saved once again, and now it reaches terminal node ofclassification sub-network, jumping to the edge of saved on-site primarydecoding sub-network (namely, the edge with the classifying label NAMEas input and output symbols), and finding the edge of the two phones(namely, ch, ix1) satisfying the requirement of saving, and reachingnode (ch, ix1), decoding of remaining part starting from this nodeagain, decoding “ni hao, Lisi, chi le ma” is completed until speechcomes to an end. Herein, Lisi can also be replaced with any other name,and however it has no effect on primary decoding sub-network. Due toprimary decoding sub-network is extremely enormous, the disadvantage canalso be avoided of the modification of hot word must accompanyingmodification of primary decoding sub-network, which saves time ofmodifying primary decoding sub-network (one or two days generally), andalso improves accuracy rate of recognition of speech of obscure words.

Corresponding to the aforementioned method, the present application hasalso published the speech recognition system to implement theaforementioned method.

The FIG. 13 is a composition schematic diagram of a certain speechrecognition system in the present application. Refer to FIG. 13, thesystem includes:

Classification module 131 is for the classification of words, to replacethe obscure words in the general language model with their classifyingmarks;

Primary network constructing module 132 is for constructing the primarydecoding sub-network according to the speech model, primary dictionaryand general language model; during constructing the primary decodingsub-network, add classifying marked nodes in the primary decodingsub-network, and connect the classifying marked nodes with other nodesby the edges;

Sub network constructing module 133 is for constructing theclassification sub-network corresponding to each classifying languagemodel according to the speech model, primary dictionary, sub networkdictionary and the classifying language model of each classification;during constructing each classification sub-network, for the links ofclassification sub-network, take the coda phone or mute of each word inthe primary dictionary as the starting position, and the onset phone ormute of each word in the primary dictionary as the ending position;

Decoder 134 is for decoding and recognizing the speech input accordingto the primary decoding sub-network, when the decoding token meets withthe classifying marked nodes, save the edge and phone before theclassifying marked node and jump to the corresponding classificationsub-network of the classifying marked node; take the phone before theclassifying marked nodes as indexes to find the starting position of theclassification sub-network, then decode and recognize the speech inputin classification sub-network from that staring position up until theending position, and save the phone before the ending position; thenjump back the edge before the classifying marked node in the primarydecoding sub-network, and take the phone before the ending position asindexes to find the starting position of the subsequent decoding, andcontinue to decode and recognize the speech input from that startingposition; output the decoding results, which include: output thecharacter string with highest probable value as the recognizing resultsof the speech input; or output the final speech recognizing resultsafter further processing of the output results in decoding.

In an embodiment, the sub network constructing module can be usedspecifically for: constructing the classification sub-networkcorresponding to each classifying language model according to the speechmodel, primary dictionary, sub network dictionary and the classifyinglanguage model of each classification; during constructing eachclassification sub-network, for the links of classification sub-network,take the last two monophonemic of each phrase in the primary dictionary,or the mute plus the phone of monophonemic word in the primarydictionary, or the mute as the starting position, and take the two onsetmonophone of each phrase in the primary dictionary, or the phone ofmonophonemic word in the primary dictionary plus mute, or the mute asthe ending position;

In the embodiment, the decoder can be used specifically for: decodingand recognizing the speech input according to the primary decodingsub-network, when the decoding token meets with the classifying markednodes, save the edge and one or two phones before the classifying markednode and jump to the corresponding classification sub-network of theclassifying mark; take the one or two phones before the classifyingmarked nodes as indexes to find the starting position of theclassification sub-network, then decode and recognize the speech inputin classification sub-network from that staring position up until theending position, and save the one or two phones before the endingposition; then jump back the edge before the classifying marked node inthe primary decoding sub-network, and take the one or two phones beforethe ending position as indexes to find the starting position of thesubsequent decoding, and continue to decode and recognize the speechinput from that starting position; output the decoding results; outputthe decoding results, which include: output the character string withhighest probable value as the recognizing results of the speech input;or output the final speech recognizing results after further processingof the output results in decoding.

FIG. 14 is a composition schematic diagram of the primary networkconstructing module. Refer to FIG. 14, the primary network constructingmodule specifically includes:

Module I is for transforming the general language model into thecorresponding WFST network;

Module II is for transforming the primary dictionary into correspondingWFST network, in which, add the corresponding edge of classifying marksbetween the starting symbol and ending symbol, and the input and outputsymbols of the corresponding edges of classifying marks shall all bemarked in classification;

Module III is for transforming the speech model into corresponding WFSTnetwork, in which, for each phone double nodes, the edge pointing toeach classifying marked node is indicted, among which, the input andoutput symbols of edge pointing to each classifying marked node are theclassifying marks; two contiguous edges after the each classifyingmarked node: considering monophone as edge of input and output symbols;

The primary network merging module is for merging the language model,primary dictionary with WFST network from the transformation of speechmodel for obtaining a WFST network as the primary decoding sub-network.

FIG. 15 is a composition schematic diagram of the sub networkconstructing module. Refer to FIG. 15, the sub network constructingmodule specifically includes:

Module IV is for transforming the classifying language model of theclassification into corresponding sub WFST network;

Module V is for transforming the sub network dictionary intocorresponding sub WFST network, in this process, the corresponding pathof words in sub network dictionary is the same as the WFST transformingways of the conventional dictionary. However, in the presentapplication, among the phonetic symbol corresponding to start symbol, asshown in FIG. 7, except original mute (identified by symbol <s>,corresponding phonetic notation is si1), it also includes further: inthe primary dictionary, phone pairs consisting of the last two phones ofeach word, phone pairs consisting of phones of each monophonemic wordand one phone at the end of each of other words, and combination of muteand phones of each monophonemic word. Phonetic symbol corresponding toend symbol, except original mute (identified by symbol </s>,corresponding phonetic notation is si1), also includes further: in theprimary dictionary, phone pairs consisting of the first two phones ofeach word, phone pairs consisting of phones of each monophonemic wordand one phone at the beginning of each of other words, and combinationof mute and phones of each monophonemic word.

Module VI is for transforming the speech model into corresponding subWFST network, for its links: take the two sequential edges which regardthe monophone as input and output symbols and consist of the last twomonophones of each word in the primary dictionary as starting position,or take the connection of the edge which regards mute as input andoutput symbols and the edge which regards the phone of each monophonemicword in primary dictionary as input and output symbols as the startingposition, or take the edge which regards mute as input and output asstarting position; take the two sequential edges which regard monophonesas input and output symbols and consist of the first two monophones ofeach word in primary dictionary as ending position, take the connectionof the edge which regards the phone of each monophonemic word in primarydictionary as input and output symbols and the edge which regards muteas input and output symbols as the ending position, or take the edgewhich regards mute as input and output symbols as ending position;

The sub network merging module is for merging the speech model, subnetwork dictionary with WFST network from the transformation of languagemodel of the classification for obtaining a WFST network as theclassification sub-network corresponding to the classification.

FIG. 16 is a composition schematic diagram of decoder of the presentapplication. Refer to FIG. 16, the decoder specifically includes:

Primary decoding module I is for decoding and recognizing the speechinput according to the primary decoding sub-network, when the decodingtoken meets with the edge which regards the classifying marks as inputand output symbols, determine the met classifying marked node, and takethe edge which regards the classifying mark as input and output symbolas the edge before the classifying marked node for preserving, and savethe two monophones before the classifying marked node;

Sub decoding module is for jumping to the classifying classificationsub-network corresponding to the classifying mark, take the one or twophones before classifying marked node as indexes to find the one or twosequential edges which regard the one or two phones as input and outputsymbols in the classification sub-network, take the one or twosequential edges as starting position, and decode and recognize thespeech input in classification sub-network from that starting position;when the decoding token meets with the edge which regards monophone asinput and output symbol, save the monophone of the edge until reachingthe ending position;

Primary decoding module II is for jumping back the edge beforeclassifying marked node of the primary decoding sub-network, and takethe one or two monophones saved before ending position as indexes, tofind the one or two sequential edges which are after classifying markednode and regard the one or two monophones before ending position asinput and output symbols respectively, take the reached nodes of the oneor two sequential edges as starting position of subsequent decoding,continue to decode and recognize the speech input from that startingposition;

Outputting module is for outputting decoding results, which specificallyincludes: output the character string with highest probable value as therecognizing results of the speech input; or output the final speechrecognizing results after further processing of the output results indecoding.

FIGS. 17A and 17B are schematic flowcharts of a method 1700 forrecognizing speech commands, in accordance with some implementations. Insome implementations, one or more of the operations described withreference to the method 1700 are performed at a device (e.g., device1808/1810, FIG. 18). In some implementations, one or more of theoperations described with reference to the method 1700 are performed ata server system (e.g., speech recognition server system 1811, FIG. 18).For ease of explanation, the method 1700 is described with reference toa device.

The method 1700 includes generating (1702) a decoding network fordecoding speech input. The decoding network includes a primarysub-network and one or more classification sub-networks. The primarysub-network includes (1704) a plurality of classification nodes, eachclassification node corresponding to a respective classificationsub-network of the one or more classification sub-networks. Furthermore,each classification sub-network of the one or more classificationsub-networks corresponds to a group of uncommon words.

In some embodiments, the decoding network is (1706) a weighted finitestate transducer.

In some embodiments, the one or more classification sub-networks include(1708) a medical terminology sub-network, a personal names sub-network,a place names sub-network, and a computer terminology sub-network.

The method 1700 further includes receiving (1710) a speech input. Thespeech input is decoded (1712) by instantiating (1714) a tokencorresponding to the speech input in the primary sub-network and passing(1716) the token through the primary network. When the token reaches arespective classification node of the plurality of classification nodes,decoding the speech input further includes transferring (1718) the tokento the corresponding classification sub-network. In some embodiments,transferring the token to the corresponding classification sub-networkfurther includes (1720) preserving one or more phones obtained prior tothe token reaching the classification node as a starting index for theclassification sub-network.

Decoding the speech input further includes passing (1722) the tokenthrough the corresponding classification sub-network. When the tokenreaches an accept node of the classification sub-network, decoding thespeech input further includes returning (1724) a result of the tokenpassing through the classification sub-network to the primarysub-network. The result includes one or more words in the group ofuncommon words corresponding to the classification sub-network. In someimplementations, returning the result of the token passing through theclassification sub-network to the primary sub-network includes (1726)preserving one or more phones obtained prior to the token reaching theaccept node of the classification sub-network as a returning index forthe primary decoding sub-network. In some embodiments, the returnedresult is (1728) a respective result in a plurality of possibletoken-passing results through the classification sub-network. Thereturned result has a higher rollback probability than any other resultin the plurality of possible token passing results through theclassification sub-network.

Finally, the method 1700 further includes outputting (1730) a stringcorresponding to the speech input that includes the one or more words.

It should be understood that the particular order in which theoperations in FIG. 1 have been described is merely exemplary and is notintended to indicate that the described order is the only order in whichthe operations could be performed. One of ordinary skill in the artwould recognize various ways to reorder the operations described herein.

FIG. 18 is a diagram of a client-server environment 1800 for speechcommand recognition, in accordance with some implementations. Whilecertain specific features are illustrated, those skilled in the art willappreciate from the present disclosure that various other features havenot been illustrated for the sake of brevity and so as not to obscuremore pertinent aspects of the implementations disclosed herein. To thatend, the client-server environment 1800 includes one or more mobilephone operators 1802, one or more internet service providers 1804, and acommunications network 1806.

The mobile phone operator 1802 (e.g., wireless carrier), and theInternet service provider 1804 are capable of being connected to thecommunication network 1806 in order to exchange information with oneanother and/or other devices and systems. Additionally, the mobile phoneoperator 1802 and the Internet service provider 1804 are operable toconnect client devices to the communication network 1806 as well. Forexample, a smart phone 1808 is operable with the network of the mobilephone operator 1802, which includes for example, a base station 1803.Similarly, for example, a laptop computer 1810 (or tablet, desktop,smart television, workstation or the like) is connectable to the networkprovided by an Internet service provider 1804, which is ultimatelyconnectable to the communication network 1806.

The communication network 1806 may be any combination of wired andwireless local area network (LAN) and/or wide area network (WAN), suchas an intranet, an extranet, including a portion of the Internet. It issufficient that the communication network 1806 provides communicationcapability between client devices (e.g., smart phones 1808 and personalcomputers 1810) and servers. In some implementations, the communicationnetwork 1806 uses the HyperText Transport Protocol (HTTP) to transportinformation using the Transmission Control Protocol/Internet Protocol(TCP/IP). HTTP permits a client device to access various resourcesavailable via the communication network 1806. However, the variousimplementations described herein are not limited to the use of anyparticular protocol.

In some implementations, the client-server environment 1800 furtherincludes a speech recognition server system 1811. Within the speechrecognition server system 1811, there is a server computer 1812 (e.g., anetwork server such as a web server) for receiving and processing datareceived from the client device 1808/1810 (e.g., speech data). In someimplementations, the speech recognition server system 1811 stores (e.g.,in a database 1814) and maintains information corresponding to aplurality of acoustic models, language models, grammatical models, andthe like (e.g., any of the models ore dictionaries shown in FIG. 3, aswell as any of the constructed networks and sub-networks).

In some implementations, the speech recognition server system 1811generates a decoding network for decoding speech input and stores thedecoding network in the database 1814. The decoding network includes aprimary sub-network and one or more classification sub-networks. Theprimary sub-network includes a plurality of classification nodes. Eachclassification node corresponds to a respective classificationsub-network of the one or more classification sub-networks, and eachclassification sub-network of the one or more classificationsub-networks corresponds to a group of uncommon words (such as medicalterminology, computer terminology, place names, and/or personal names).The speech recognition system receives a speech input, for example, froma client device 1808/1810 and decodes the speech input by instantiatinga token corresponding to the speech input in the primary sub-network.The token is passed through the primary network, and when the tokenreaches a respective classification node of the plurality ofclassification nodes, the token is transferred to the correspondingclassification sub-network. The speech recognition server system 1811then passes the token through the corresponding classificationsub-network. When the token reaches an accept node of the classificationsub-network, the result of the token passing through the classificationsub-network is returned to the primary sub-network. The result includesone or more words in the group of uncommon words corresponding to theclassification sub-network. Finally, speech recognition server system1811 outputs (e.g., to back to the client device 1808/1810) a stringcorresponding to the speech input that includes the one or more words.

Those skilled in the art will appreciate from the present disclosurethat any number of such devices and/or systems may be provided in aclient-server environment, and particular devices may be altogetherabsent. In other words, the client-server environment 1800 is merely anexample provided to discuss more pertinent features of the presentdisclosure. Additional server systems, such as domain name servers andclient distribution networks may be present in the client-serverenvironment 1800, but have been omitted for ease of explanation.

FIG. 19 is a diagram of an example implementation of the device1808/1810 for speech command recognition, in accordance with someimplementations. While certain specific features are illustrated, thoseskilled in the art will appreciate from the present disclosure thatvarious other features have not been illustrated for the sake of brevityand so as not to obscure more pertinent aspects of the implementationsdisclosed herein.

To that end, the device 1808/1810 includes one or more processing units(CPU's) 1904, one or more network or other communications interfaces1908, a display 1901, memory 1906, a microphone 1909, one or more mobilestorage devices 1903, and one or more communication buses 1905 forinterconnecting these and various other components. The communicationbuses 1905 may include circuitry (sometimes called a chipset) thatinterconnects and controls communications between system components.Memory 1906 includes high-speed random access memory, such as DRAM,SRAM, DDR RAM or other random access solid state memory devices; and mayinclude non-volatile memory, such as one or more magnetic disk storagedevices, optical disk storage devices, flash memory devices, or othernon-volatile solid state storage devices. Memory 1906 may optionallyinclude one or more storage devices remotely located from the CPU(s)1904. Memory 1906, including the non-volatile and volatile memorydevice(s) within memory 1906, comprises a non-transitory computerreadable storage medium.

In some implementations, memory 1906 or the non-transitory computerreadable storage medium of memory 1906 stores the following programs,modules and data structures, or a subset thereof including an operatingsystem 1916, a network communication module 1918, and a speechrecognition client module 1920.

The operating system 1916 includes procedures for handling various basicsystem services and for performing hardware dependent tasks.

The network communication module 1918 facilitates communication withother devices via the one or more communication network interfaces 1908(wired or wireless) and one or more communication networks, such as theinternet, other wide area networks, local area networks, metropolitanarea networks, and so on.

In some implementations, the speech command recognition client module1920 includes a user interface sub-module 1922 for speech commandrecognition (e.g., a user activates a predefined affordance to bring upa speech command recognition user interface). To this end, the userinterface sub-module includes a set of instructions 1922-1 (e.g., fordisplaying a user interface on the display 1901, receiving user inputs,etc.) and, optionally, metadata 1922-2. In some implementations, thespeech command recognition client module 1920 includes a receivingsub-module 1924 having a set of instructions 1924-1 (e.g., forinterfacing with the microphone 1909 to receive a speech input) and,optionally, metadata 1924-2, as well as a transmitting sub-module 1926having a set of instructions 1926-1 (e.g., for interfacing with thenetwork interface 1908 to transmit the speech input to a soundrecognition server system 1811) and, optionally, metadata 1926-2.

FIG. 20 is a block diagram illustrating a speech recognition serversystem 1811, discussed above with reference to FIG. 18, in accordancewith some implementations. While certain specific features areillustrated, those skilled in the art will appreciate from the presentdisclosure that various other features have not been illustrated for thesake of brevity and so as not to obscure more pertinent aspects of theimplementations disclosed herein.

To that end, the speech recognition server system 1811 includes one ormore processing units (CPU's) 2002, one or more network or othercommunications interfaces 2008, memory 2006, and one or morecommunication buses 2004 for interconnecting these and various othercomponents. The communication buses 2004 may include circuitry(sometimes called a chipset) that interconnects and controlscommunications between system components. Memory 2006 includeshigh-speed random access memory, such as DRAM, SRAM, DDR RAM or otherrandom access solid state memory devices; and may include non-volatilememory, such as one or more magnetic disk storage devices, optical diskstorage devices, flash memory devices, or other non-volatile solid statestorage devices. Memory 2006 may optionally include one or more storagedevices remotely located from the CPU(s) 2002. Memory 2006, includingthe non-volatile and volatile memory device(s) within memory 2006,comprises a non-transitory computer readable storage medium.

In some implementations, memory 2006 or the non-transitory computerreadable storage medium of memory 2006 stores the following programs,modules and data structures, or a subset thereof including an operatingsystem 2016, a network communication module 2018, a speech commandrecognition server module 2020.

The operating system 2016 includes procedures for handling various basicsystem services and for performing hardware dependent tasks.

The network communication module 2018 facilitates communication withother devices (e.g., other speech recognition server system 1811 as wellas client devices 1808/1810) via the one or more communication networkinterfaces 2008 (wired or wireless) and one or more communicationnetworks, such as the Internet, other wide area networks, local areanetworks, metropolitan area networks, and so on.

The speech command recognition module server 2020 is configured toreceive sound samples, train acoustic models, and decode sample samples.To that end, the speech command recognition server module 2020optionally includes one or more sub-modules, each including a set ofinstructions and optionally including metadata. For example, in someimplementations, the speech command recognition server module 2020receives sound samples from a client 1808/1810 using a receivingsub-module 2024 (which includes a set of instructions 2024-1 andmetadata 2024-2), trains the acoustic models with the received soundsamples using a training sub-module 2022 (which includes a set ofinstructions 2022-1 and metadata 2022-2) and decodes subsequent soundsamples using a decoding sub-module 2026 (which includes a set ofinstructions 2026-1 and metadata 2026-2) As an example of metadata, insome implementations, the metadata 2010-1 includes language settingscorresponding to respective users, effectiveness ratings provided by therespective users, etc.

While particular embodiments are described above, it will be understoodit is not intended to limit the invention to these particularembodiments. On the contrary, the invention includes alternatives,modifications and equivalents that are within the spirit and scope ofthe appended claims. Numerous specific details are set forth in order toprovide a thorough understanding of the subject matter presented herein.But it will be apparent to one of ordinary skill in the art that thesubject matter may be practiced without these specific details. In otherinstances, well-known methods, procedures, components, and circuits havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

The terminology used in the description of the invention herein is forthe purpose of describing particular embodiments only and is notintended to be limiting of the invention. As used in the description ofthe invention and the appended claims, the singular forms “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will also be understood that theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “includes,” “including,”“comprises,” and/or “comprising,” when used in this specification,specify the presence of stated features, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

Although some of the various drawings illustrate a number of logicalstages in a particular order, stages that are not order dependent may bereordered and other stages may be combined or broken out. While somereordering or other groupings are specifically, others will be obviousto those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method of recognizing speech, comprising:generating a decoding network for decoding speech input, the decodingnetwork comprising a primary sub-network and one or more classificationsub-networks, wherein: the primary sub-network includes a plurality ofclassification nodes, each classification node corresponding to arespective classification sub-network of the one or more classificationsub-networks; and each classification sub-network of the one or moreclassification sub-networks corresponds to a group of uncommon words;receiving a speech input; and decoding the speech input by:instantiating a token corresponding to the speech input in the primarysub-network; passing the token through the primary network; when thetoken reaches a respective classification node of the plurality ofclassification nodes, transferring the token to the correspondingclassification sub-network; passing the token through the correspondingclassification sub-network; when the token reaches an accept node of theclassification sub-network, returning a result of the token passingthrough the classification sub-network to the primary sub-network,wherein the result includes one or more words in the group of uncommonwords corresponding to the classification sub-network outputting astring corresponding to the speech input that includes the one or morewords.
 2. The method of claim 1, wherein the returned result is arespective result in a plurality of possible token-passing resultsthrough the classification sub-network, the returned result having ahigher rollback probability than any other result in the plurality ofpossible token passing results through the classification sub-network.3. The method of claim 1, wherein: transferring the token to thecorresponding classification sub-network further includes preserving oneor more phones obtained prior to the token reaching the classificationnode as a starting index for the classification sub-network; andreturning the result of the token passing through the classificationsub-network to the primary sub-network includes preserving one or morephones obtained prior to the token reaching the accept node of theclassification sub-network as a returning index for the primary decodingsub-network.
 4. The method of claim 1, wherein the decoding network is aweighted finite state transducer.
 5. The method of claim 1, wherein theone or more classification sub-networks include a medical terminologysub-network, a personal names sub-network, a place names sub-network,and a computer terminology sub-network.
 6. An electronic device,comprising: one or more processors; memory; and one or more programs,wherein the one or more programs are stored in memory and configured tobe executed by the one or more processors, the one or more programsincluding an operating system and instructions that when executed by theone or more processors cause the electronic device to: generate adecoding network for decoding speech input, the decoding networkcomprising a primary sub-network and one or more classificationsub-networks, wherein: the primary sub-network includes a plurality ofclassification nodes, each classification node corresponding to arespective classification sub-network of the one or more classificationsub-networks; and each classification sub-network of the one or moreclassification sub-networks corresponds to a group of uncommon words;receive a speech input; and decode the speech input by: instantiating atoken corresponding to the speech input in the primary sub-network;passing the token through the primary network; when the token reaches arespective classification node of the plurality of classification nodes,transferring the token to the corresponding classification sub-network;passing the token through the corresponding classification sub-network;when the token reaches an accept node of the classification sub-network,returning a result of the token passing through the classificationsub-network to the primary sub-network, wherein the result includes oneor more words in the group of uncommon words corresponding to theclassification sub-network output a string corresponding to the speechinput that includes the one or more words.
 7. The electronic device ofclaim 6, wherein the returned result is a respective result in aplurality of possible token-passing results through the classificationsub-network, the returned result having a higher rollback probabilitythan any other result in the plurality of possible token passing resultsthrough the classification sub-network.
 8. The electronic device ofclaim 6, wherein: transferring the token to the correspondingclassification sub-network further includes preserving one or morephones obtained prior to the token reaching the classification node as astarting index for the classification sub-network; and returning theresult of the token passing through the classification sub-network tothe primary sub-network includes preserving one or more phones obtainedprior to the token reaching the accept node of the classificationsub-network as a returning index for the primary decoding sub-network.9. The electronic device of claim 6, wherein the decoding network is aweighted finite state transducer.
 10. The electronic device of claim 6,wherein the one or more classification sub-networks include a medicalterminology sub-network, a personal names sub-network, a place namessub-network, and a computer terminology sub-network.
 11. Anon-transitory computer readable storage medium storing one or moreprograms, the one or more programs comprising instructions, which whenexecuted by an electronic device with one or more processors and memory,cause the electronic device to: generate a decoding network for decodingspeech input, the decoding network comprising a primary sub-network andone or more classification sub-networks, wherein: the primarysub-network includes a plurality of classification nodes, eachclassification node corresponding to a respective classificationsub-network of the one or more classification sub-networks; and eachclassification sub-network of the one or more classificationsub-networks corresponds to a group of uncommon words; receive a speechinput; and decode the speech input by: instantiating a tokencorresponding to the speech input in the primary sub-network; passingthe token through the primary network; when the token reaches arespective classification node of the plurality of classification nodes,transferring the token to the corresponding classification sub-network;passing the token through the corresponding classification sub-network;when the token reaches an accept node of the classification sub-network,returning a result of the token passing through the classificationsub-network to the primary sub-network, wherein the result includes oneor more words in the group of uncommon words corresponding to theclassification sub-network output a string corresponding to the speechinput that includes the one or more words.
 12. The non-transitorycomputer readable storage medium of claim 11, wherein the returnedresult is a respective result in a plurality of possible token-passingresults through the classification sub-network, the returned resulthaving a higher rollback probability than any other result in theplurality of possible token passing results through the classificationsub-network.
 13. The non-transitory computer readable storage medium ofclaim 11, wherein: transferring the token to the correspondingclassification sub-network further includes preserving one or morephones obtained prior to the token reaching the classification node as astarting index for the classification sub-network; and returning theresult of the token passing through the classification sub-network tothe primary sub-network includes preserving one or more phones obtainedprior to the token reaching the accept node of the classificationsub-network as a returning index for the primary decoding sub-network.14. The non-transitory computer readable storage medium of claim 11,wherein the decoding network is a weighted finite state transducer. 15.The non-transitory computer readable storage medium of claim 11, whereinthe one or more classification sub-networks include a medicalterminology sub-network, a personal names sub-network, a place namessub-network, and a computer terminology sub-network.